THE DIFFERENCE BETWEEN DATA POSERS
AND (REAL) DATA PLAYERS

Key Takeaways:

Be on the look out for people who over-use Buzzwords. They use them because they are an inch deep and are flashy. Always ask those people to prove the value of those buzzwords.

A specific buzzword, especially today, is A.I. (Artificial Intelligence). Posers love to throw that word around for pretty much anything related to intelligence or automated scoring. They use it because it sells but they don’t really know what it means

Identifying a Non-Poser is easy because Non-Poser’s, or Data Players, are goal-oriented people. They look at the what the problem is, set the problem statement, gather data, and then see if they have enough data to solve the problem in the first place.

Posers on the other hand just want to sell whatever they can and make as much as they can off that sale. As stated before they like to use buzzwords and when challenged on those buzzwords they most likely can not defend the implementation of high priced systems like large CRMs.

Posers do not care about your challenges. They set unrealistic expectations and do not go into detail as to how their solution solves your problems.

Below is a lightly edited transcript of Episode 20 of the Inevitable Success Podcast with Damian and special guest Stephen Yu. (Listen Here)

Transcript

Damian: Here I am today with Stephen Yu

Stephen: Hello and good afternoon.

Damian: Today we’re going to talk about exposing the fakes and the posers in the world of data.

Stephen: There are a lot of posers in this industry. The easiest way to identify a poser is buzzwords, posers love them and use them a lot. They’re all an inch deep.

Stephen: Yeah always ask the question, “Well great how are we going to make money using Big Data?” Another big one is A.I. these days. Anything and everything related to any algorithmic thing, even when it is not algorithmic, people still call it A.I.

Damian: Why is that?

Stephen: Because it sells.

Damian: It sells, ok. Well what is A.I., and it can be a definition for you no need to give us a Webster’s definition.

Stephen: So artificial intelligence. Well they think about things for you. Unfortunately, we’re not there yet. To me it is about automation really. In other words, you do certain things again and again and you go like I don’t want to make any decisions, so I let it go. The machine will pick it up and do things if is wrong it will learn all by itself and then it gets better.

Damian: Right. I mean we kind of mock some of the A.I. things that we see people doing. I know I do anyway, because when you look at it you can see that it is just conditional logic, not A.I. Sometimes it is just an Excel lookup table.

Stephen: Posers call just about anything related to intelligence or automated scoring, A.I. The best thing was when big data was really big like 3-4 years ago. Before Big Data became a term I had to explain what I did for a living. I use to say, “So yeah, we analyze the data, we summarize the data ,we clean the data, we get intelligence out of it, it will give you some action and then you do things and you measure it and you make it better as we go along.”

Now that’s a long-winded way to say the same thing. But instead all I had to say was, “I’m in the big data space” and I realized that it worked with my uncle with my own mother. It worked with a lot of people. So why not.

Damian: I found out a couple of months ago that for years my dad has been saying that I work at Google because I do a lot of work with Google Analytics. He just never went deeper than that. He was like yeah, my son works at Google.

Stephen: A lot of my mother’s friends they all say, “You know, I don’t know what my son does, but he works on a computer all day long.”

Damian: I think they’re just happy that son doesn’t live with them anymore.

Stephen: I’m extremely happy my kids moved out, by the way. But the point is the buzzword. How do you spot a poser? Well let’s talk about why posers are bad, they are bad because posers don’t bring any substance to the conversation.

So, what is the difference between posers and non-posers. Non-posers, in the analytics and data world are what we call goal-oriented people. They look at what the problem is, set the problem statement properly, and then they gather the data to see if they have enough data to solve aforementioned problem. Then they lay out their road map. They ask the questions, “What do you have to do now?” “What kind data is in your data set?” “Do you have to fix the data in some ways? Consolidate it? ” Then you run the report to see what’s what. It’s like taking an X-ray picture of a patient really. Then you lay out steps to achieve your goals and you actually help people doing it. Then you measure the success. It could be an improvement or just the status quo. But you learn from such tests and next round you make it better. So, goal-oriented people start with a proper problem statement.

Posers, they just want to sell what they have. They are mostly one trick ponies, so they say ‘Oh yeah if you just do this all your dreams will come true’. Not only did they drop buzzwords that whole time, they over promise. It’s funny that we are in CRM space, it was a hot word around the 80s and early 90s people used to spend like seven figures to get a State-of-the-art CRM. It became a bad bad word for a lot of organizations. What happened was that you buy all these things, and everybody paid that kind of money based on over promise. And they quickly realize that they will never recover all that money that they spent based on a 1 or 2 percent improvement. They had to ask, “How am I going to make up all that money that I spent?”

Damian: Yeah that’s one of the best exercises to go through. Ask yourself, “What level of performance increase would I need to see to justify that?” “Just to even break-even?” Because sometimes you really don’t have a choice since there’s an entry to do these things. And a lot of the times you just don’t have the data set yet for that.

Stephen: You have to be honest about that. You know, what is your channel? Okay fine you’re using really expensive channel so a 1% decrease or increase in response rate may mean something. But if your channel is so cheap, for example you use Facebook for acquisition, “Do you need to increase the accuracy when targeting through Facebook?” Yes, you do. But that improvement is not about cost cutting because you still pay whatever you have to pay to Facebook.

Damian: Well you can get more efficient targeting and cut waste that way.

Stephen: But that efficiency improvement should be able to pay for the services that you have.

Damian: You can do some pretty efficient things without that tool set. And I think at least one of the things I’ve always heard you talk about is that somebody who is, you know, a true professional in that space, they ask those types of questions, can I justify your investment in me. And that’s what we’re doing.

Stephen: So another clue that somebody is not a poser they always have to have a goal-oriented mindset. We talked about that. Then you have to define the jobs to carry that out. In other words, positions or job description before the names.

So good consultants or good analysts should really define what the job is without thinking about his own name being on the list. So, you should be able to fire yourself. When talking about any kind of expensive initiative, you should be able to say things like, “It’s all good down the line but, number one you don’t have enough data to build the model with or your universe is so small that maybe we’ll talk about it as you grow a little bit more down the line.”

Whatever the reason may be you cannot just say “I’m going to build this complex machine learning algorithm because I can and I’m going to charge like $200 dollars an hour doing it. That’s just wrong.

Damian: You made me think of like the opposite side of that, right being the company. So, let me explain that, if you work at an organization that respects that thinking that says, “Yep I’m going to try to design this and I may put myself out of a job but you know because I’m that kind of solver and there’ll be another job for me. If you work in an organization like that, that’s awesome. If you don’t and you’re not a poser, go somewhere else that respects that.

Stephen: Some cultures breed posers I guess, because that’s how you survive.

Damian: There’s a lot of places that you can hide in the hierarchy of not getting anything done and talking about stuff.

Stephen: This is all a little off topic but the question that we always ask is knowing how to survive in an organization is that a skill, one may say that it is. It depends on where you work. But we’re in the business of finding, I’m sorry about using this cliché but, we’re in the business of finding actionable insights out of mounds of data. It’s really that simple.

If you find an actionable insight then you take an action on it, that is the first step. So please be careful with people who just come in and say that you know if you buy these tools that all of your dreams will come true. I heard this interview with Mark Knopfler the lead guitar player in Dire Straits. The interviewer brought out this book called ‘Master Your Guitar in a Week’. And then he saw this book. You know what I bought this book. And it’s a total lie. This is not going to happen. But they sold books. Do they have any repeat buyers? I highly doubt it.

Damian: As a fellow guitar player, I don’t think I could put a chord together in the first week.

Stephen: There’s no way to do it. The reason posers are able to sell things is because users do not always want to do anything. There are no magic bullets really. You’ve got to do something. Maybe what got automated is all that number crunching. You don’t have to know programming language to understand all these things or things that used to be a long process is now down to a finger snap and all that.

By the way we sell such products ourselves. The things that we do so easily here. If you dial back only 10-20 years when the word CRM was still popular. It’s still popular now and is coming back with the cost having decreased so much.

So, it got easier. But it’s also easy to abuse and it got so far ahead that people will say, “So, I gave you the data and you’re going to give me all the reports, models, segments and all these things. But I have to pick like what the next section should be”? Well, you got to do some thinking. In other words, I will give you the multiple-choice answer.

The biggest misnomer about A.I. and all that is that if you just plug in the data it will do everything and even set up the marketing strategy for you. We’re not there yet. Will we get there one day? I think we can. Is it there yet? No.

I’ll give you a quick example about how machine intelligence really works. It was big news a little while back how A.I. beat the smartest human being the game of Go. Yes, well who gave the machine that purpose? We did. The machine did not pick it. Then it had to learn the boundaries of the game, the rules of the game, and all that, then practice it within the boundary and got really good at it.

Such boundaries needed to be set by human beings. So, we’ve got to do something. And also, no matter how easy all this data manipulation and data mining, how easy it became. That doesn’t mean that it can’t answer logical questions. You have to somehow have some logic in your head, “Am I asking the right question?” So, when the answer comes back if the machines are targeting the frequent buyers who are not actually big spenders. What are you going to do about it? These are the challenges that will come up at some point, you have to stop and say OK, so I have to change the way I talk to these people. Now that’s a human interaction. So, because this is this and that is that and the logical conclusion is that we have to treat them separately. Well you’ve got to make that conclusion. Now, consultants will do that for you. But as we eliminate…

Damian: The ones that aren’t posers.

Stephen: Well yeah exactly, because posers will say just buy this tool and pay 20 grand or whatever and all your dreams will come true. It will build models all by itself. I’ve seen some ESP’s, pretty famous ones, that claim such things. Oh, not only will it deploy all the emails, you know it will learn how it did and based on their learning we will do better. So what kind of data do you click? Turns out that they’re just making up some rules based on the number of days since the last click and stuff like that. That is not machine learning.

Somebody set up the rule and then the machine is just following the rules. It’s not anything new. I’m sorry.

Damian: We joke about that sometimes we say that’s not A.I. that’s R.I., Real intelligence.

Stephen: And hey that works too. But just don’t collect all of that money, you know, by bamboozling your users. Don’t do that.

Damian: OK. We’ll probably wrap this up, but I know you were conducting an interview today. So maybe you have some questions fresh in your head of what you would ask somebody to find out if they’re goal oriented.

Stephen: Yeah. So, watch for the one trick pony and people who keep pushing the product not asking how you feel or what are your challenges. So, it’s not any different from picking a good doctor. If the doctor is pushing only one type of drug maybe they getting a kickback from some pharmaceutical company. So that’s a sign of a quack. It’s the same thing really. Does he really listen to you? Or is it about your business challenges or more about feature functionality of the specific tool-sets, and by the way we can fall into that trap ourselves. We have tool sets that we sell. But what it does is different for everybody because we have a goal-oriented mindset.

Maybe you have a one-time buyer a problem or you have an acquisition problem, or you have a value problem all your valuable customers are not coming back. All those things are different challenges and the way you use a tool is different based on your challenges. But it’s not like oh well so just pay us X Y Z amount of money every month and all your dreams will come true. No, you have to set the goals yourself. I’m sorry, we’ll help you set the goals we’ll help you prioritize these things. But the posers will say don’t worry about it, we’ll just take care of it.

Damian: So it sounds like there’s a pattern of setting high or maybe even unrealistic expectations and not wanting to go into great depth about what your challenges or goals are. They probably do a lot of talking not a lot of emphatic listening.

Stephen: Yeah, that’s true. That’s one of the dead giveaways.

Damian: So, we’ll wrap it up here. But, you know we’re going to keep our eyes out for you guys out there. I know none of our listeners are posers.

Stephen: Great.

Damian: If you enjoy today’s episode we ask that you please leave rating and write a review. Or better yet share with another marketer. Be sure to subscribe to the podcast for new episodes. Also check out the show description for complete show notes and links to all resources covered in today’s episode.